Information Theory and Machine Learning
The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be...
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| Formato: | Online |
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| Idioma: | inglês |
| Publicado em: |
MDPI - Multidisciplinary Digital Publishing Institute
2022
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| Acesso em linha: | ONIX_20221025_9783036553078_107 |
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| _version_ | 1869515219666993152 |
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| collection | Directory of Open Access Books |
| description | The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems. |
| format | Online |
| id | doab-20.500.12854ir-93254 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-932542024-04-09T23:15:55Z Information Theory and Machine Learning Zheng, Lizhong Tian, Chao supervised classification independent and non-identically distributed features analytical error probability empirical risk generalization error K-means clustering model compression population risk rate distortion theory vector quantization overfitting information criteria entropy model-based clustering merging mixture components component overlap interpretability time series prediction finite state machines hidden Markov models recurrent neural networks reservoir computers long short-term memory deep neural network information theory local information geometry feature extraction spiking neural network meta-learning information theoretic learning minimum error entropy artificial general intelligence closed-loop transcription linear discriminative representation rate reduction minimax game fairness HGR maximal correlation independence criterion separation criterion pattern dictionary atypicality Lempel–Ziv algorithm lossless compression anomaly detection information-theoretic bounds distribution and federated learning thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems. 2022-10-25T09:04:09Z 2022-10-25T09:04:09Z 2022 book ONIX_20221025_9783036553078_107 9783036553078 9783036553085 https://directory.doabooks.org/handle/20.500.12854/93254 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/6152 https://mdpi.com/books/pdfview/book/6152 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-5308-5 10.3390/books978-3-0365-5308-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036553078 9783036553085 254 open access |
| spellingShingle | supervised classification independent and non-identically distributed features analytical error probability empirical risk generalization error K-means clustering model compression population risk rate distortion theory vector quantization overfitting information criteria entropy model-based clustering merging mixture components component overlap interpretability time series prediction finite state machines hidden Markov models recurrent neural networks reservoir computers long short-term memory deep neural network information theory local information geometry feature extraction spiking neural network meta-learning information theoretic learning minimum error entropy artificial general intelligence closed-loop transcription linear discriminative representation rate reduction minimax game fairness HGR maximal correlation independence criterion separation criterion pattern dictionary atypicality Lempel–Ziv algorithm lossless compression anomaly detection information-theoretic bounds distribution and federated learning thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology Information Theory and Machine Learning |
| title | Information Theory and Machine Learning |
| title_full | Information Theory and Machine Learning |
| title_fullStr | Information Theory and Machine Learning |
| title_full_unstemmed | Information Theory and Machine Learning |
| title_short | Information Theory and Machine Learning |
| title_sort | information theory and machine learning |
| topic | supervised classification independent and non-identically distributed features analytical error probability empirical risk generalization error K-means clustering model compression population risk rate distortion theory vector quantization overfitting information criteria entropy model-based clustering merging mixture components component overlap interpretability time series prediction finite state machines hidden Markov models recurrent neural networks reservoir computers long short-term memory deep neural network information theory local information geometry feature extraction spiking neural network meta-learning information theoretic learning minimum error entropy artificial general intelligence closed-loop transcription linear discriminative representation rate reduction minimax game fairness HGR maximal correlation independence criterion separation criterion pattern dictionary atypicality Lempel–Ziv algorithm lossless compression anomaly detection information-theoretic bounds distribution and federated learning thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| topic_facet | supervised classification independent and non-identically distributed features analytical error probability empirical risk generalization error K-means clustering model compression population risk rate distortion theory vector quantization overfitting information criteria entropy model-based clustering merging mixture components component overlap interpretability time series prediction finite state machines hidden Markov models recurrent neural networks reservoir computers long short-term memory deep neural network information theory local information geometry feature extraction spiking neural network meta-learning information theoretic learning minimum error entropy artificial general intelligence closed-loop transcription linear discriminative representation rate reduction minimax game fairness HGR maximal correlation independence criterion separation criterion pattern dictionary atypicality Lempel–Ziv algorithm lossless compression anomaly detection information-theoretic bounds distribution and federated learning thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues::TBX History of engineering and technology |
| url | ONIX_20221025_9783036553078_107 |